liu_2023_purchase_intention
1,632 responses from 544 respondents to 3 items.
About this table
| Description | 3-item Purchase Intention subscale (1-5 Likert), same survey as liu_2023_brand_trust |
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| Reference | Liu Q, Wang X (2023) The impact of brand trust on consumers' behavior toward agricultural products' regional public brand. PLoS ONE. |
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| DOI | 10.1371/journal.pone.0295133 |
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| Licence | CC BY 4.0 |
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| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0295133.s001 |
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Size and shape
| Responses | 1,632 |
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| Respondents | 544 |
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| Items | 3 |
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| Response categories | 5 |
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| Responses per respondent | 3 |
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| Responses per item | 544 |
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| Density | 1 |
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| Longitudinal | FALSE |
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Classification
| measurement tool | Survey/questionnaire |
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| item format | Likert Scale/selected response |
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| primary language(s) | chi |
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Item text
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Purchase Intention toward agricultural products' regional public brand (3 items adapted from Sun et al. 2022 and Ajzen 2020) |
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| Mean words per item | 1 |
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| Mean characters per item | 2 |
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| Mean characters per response | 7.400 |
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| Flesch-Kincaid grade level | -3.400 |
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Get the data
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("liu_2023_purchase_intention")
# Python
pip install irw
import irw
df = irw.fetch("liu_2023_purchase_intention")
Version and provenance
| IRW version | v393 |
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| Redivis dataset | item_response_warehouse_3 v7.0 |
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| Redivis dataset DOI | 10.57761/pqqn-pm43 |
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| Manifest pin for this IRW version | v7.0 |
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| Metadata source | irw_meta v23.0 |
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